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1089 articles for “data modelling”
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Fake Cryptocurrency Detection Using Python
Abstract: This study investigates the use of Python-based techniques for detecting fraudulent cryptocurrencies, addressing a growing concern in the digital financial ecosystem. The research methodology integrates various data science approaches, including web scraping, API integration, and advanced data analysis using Pandas and NLTK. Machine learning models, particularly classification algorithms such as Random Forest, are employed to analyze key features extracted from cryptocurrency whitepapers, social media discussions, and transactional data. By training …
Published in Recent Trends in Programming languages · Vol. 12, Issue 1, 2025 · pp. 1–7 Read article
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Prediction of Customer Churn Using Machine Learning Classification Models
Abstract: Customer churn prediction is a critical task in both the telecommunication and medical industries, where retaining customers or patients is essential for ensuring long-term profitability and maintaining high-quality service. To address this, a range of machine learning models—including logistic regression, decision trees, random forests, gradient boosting machines, and support vector machines—were employed to accurately forecast churn behavior. Prior to model training, the dataset underwent thorough preprocessing, which included handling missing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 86–92 Read article
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GenChrome-ML: A Machine Learning Framework for Early Detection of Chromosomal Disorders Using Genomic Data
Abstract: The increasing burden of chronic disease and cancer demands innovative, more rapid and effective diagnostic tools in the field of healthcare. The majority of current diagnostic tools are dependent upon clinical symptomology and manual evaluation, leading to delays in early detection and treatment. The development of artificial intelligence (AI) and machine learning (ML), in recent years, has offered opportunities for the enhancement of disease prediction, diagnosis and personalization of treatment …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Real-Time Cab Fare and ETA Prediction Using API Integration
Abstract: The exponential proliferation of ride-hailing platforms has necessitated the formulation of sophisticated and highly responsive predictive models for cab fare estimation and estimated time of arrival (ETA) computation. This work elucidates a robust framework leveraging real-time application programming interface (API) integration from Uber and Ola within a Flutter-based ecosystem to enhance predictive analytics. By assimilating real-time geospatial data, dynamic pricing algorithms, and latency-optimized API responses, this study investigates the empirical …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 08–15 Read article
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Social Media Analysis Using Big Data
Abstract: Social media has become an essential aspect of everyday life across different age groups, serving functions ranging from sharing personal updates to staying informed about social developments. Our objective is to harness big data to extract meaningful insights from the immense and ever-expanding volume of data generated on social media platforms. We will collect and analyze data from various social media sources, including text, images, and user interactions, using cutting-edge …
Published in Journal of Advanced Database Management & Systems · Vol. 11, Issue 3, 2024 · pp. 35–46 Read article
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AI-Based Discovery of High-Performance Energy Storage Polymer Composites: A Comprehensive Review
Abstract: The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1083–1097 Read article
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A Comprehensive Review on Federated Learning in Disease Detection
Abstract: Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 1, 2026 · pp. 1–21 Read article
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Mindwell: A Psychological Guide for Well-being
Abstract: Mental health is a crucial aspect of overall well-being, yet access to professional therapy remains a significant challenge for many individuals due to various barriers, including cost, availability, and stigma. This research aims to develop an accessible and effective mental health therapy chatbot, named Mindwell Psychology, leveraging the power of large language models (LLMs) and state-of-the-art natural language processing techniques. The primary objective of this study is to create a …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 63–77 Read article
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Evaluating the Efficiency of LLMs-SA (Sentiment Analysis) via Social Media Texts
Abstract: Sentiment analysis (SA) is becoming popular in business and scientific communities as the processing of natural language (NLP), computational linguistics, text analytics, image-based processing or video- based processing is used in extracting and mining subjective information in the web, social network, etc. It is able to detect positive, negative or neutral information and can be selected to absorb polarity, sentiments, urgency and goals of mount importance. The majority of the …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 Read article
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Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems
Abstract: Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 391–402 Read article
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Identification of Papaya Fruit Ripening Process Using AI
Abstract: Identifying the ripening process of papaya fruit using artificial intelligence involves employing machine learning algorithms to analyze various features such as color changes, texture alterations and chemical compositions. This model is capable of analyzing visual cues to determine the stage of ripeness. The dataset compares images of papaya at various ripening stages, and our AI model demonstrated high accuracy in classifying these stages. Employing machine learning algorithms and image processing …
Published in Research & Reviews : Journal of Food Science & Technology · Vol. 13, Issue 2, 2024 · pp. 23–30 Read article
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Role of Satellite Data Assimilation on ERA-Interim and ERA5 Wave Parameter Ratios – A Case Study based on Year-long In-Situ Observations in the Bay of Bengal
Abstract: The rapid decline in the energy resources forced mankind to tap other forms of natural energy resources in the light of exponential increase in the demand due to over-population. The energy from ocean waves is one of the cleanest sources of energy available perennially that changes seasonally and is site-specific. To assess the wave power potential at any site, knowledge of the wave parameters such as wave height, period and …
Published in Journal of Remote Sensing & GIS · Vol. 16, Issue 1, 2025 · pp. 1–20 Read article
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Machine Learning Based Optimization of Polymer Structure Property Relationships in Composite Material Systems
Abstract: In modern engineering applications, polymer-based composite materials have garnered a lot of attention because of their lightweight nature, high strength-to-weight ratio, and changing physical features. In order to maximize the relationships between polymer structure and properties in composite materials, this study suggests a strategy based on reinforcement learning (RL). The research utilized the Polymer Composite Properties Dataset, which contains 12,700 records associated with polymer matrices, reinforcement fillers, interfacial bonding characteristics, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 242–255 Read article
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Very Short-Term Load Forecasting Using Gaussian Process Regression
Abstract: Very Short-Term Load Forecasting (VSTLF) is critical for real-time grid stability, frequency control, and economic dispatch. This study proposes a Gaussian Process Regression (GPR)-based framework for one-hour-ahead load forecasting using hourly data from January 2020 to April 2024 for Delhi, India. The model incorporates meteorological data such as temperature, humidity, and dew point with lagged load values. The research takes into account time-related dependencies and seasonal changes in order to …
Published in Trends in Electrical Engineering · Vol. 16, Issue 1, 2025 · pp. 91–104 Read article
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Support Vector Machine Inspired Load Forecasting of a State University in Haryana
Abstract: Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 33–40 Read article
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Deep Learning-Based Pneumonia Diagnosis: A Comparative Review of Models and Metrics
Abstract: Pneumonia is a common viral infection that affects a large percentage of people worldwide. It is more common in developing and impoverished areas because of factors like poor sanitation, crowded living quarters, pollution in the environment, and restricted access to medical facilities. In order to improve survival chances and gain access to therapeutic therapies, pneumonia must be diagnosed as soon as possible. A type of artificial intelligence called deep learning …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 3, 2024 Read article
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Enhancing Wildlife Tourism Management Using Deep Learning and Particle Swarm Optimization (PSO) for Animal Detection in Wildlife Sanctuaries
Abstract: Wildlife tourism is one of the most thriving sectors, faced with huge challenges in terms of safeguarding protected areas. As demand for wildlife experiences accelerates, it becomes necessary to find efficient measures that are friendly to conservation. The use of these advanced techniques in this field such as YOLO and PSO algorithm presents a new dimension on managing wildlife tourism. To harness the abilities of these techniques, this research centers …
Published in Current Trends in Signal Processing · Vol. 14, Issue 3, 2024 · pp. 41–50 Read article
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An Innovative Approach to Find the Optimum Lubricant for Diverse Applications Based on Scikit-Learn Library Using Python
Abstract: This paper presents an innovative approach for finding the optimum lubricant using the Scikit-learn library in Python. The proposed approach uses a linear regression model to analyze a dataset of lubricant properties and performance, specifically the viscosity, wear, and friction. The model is trained on the dataset to predict the wear and friction for a given viscosity, which can be used to identify the optimum lubricant. By analyzing a dataset …
Published in Journal of Polymer & Composites · Vol. 13, Issue 3, 2025 · pp. 25–35 Read article
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Deep Learning Architectures for Predictive Modeling in Financial Time Series
Abstract: This study investigates the application of deep learning architectures, particularly convolutional neural networks (CNNs), to the challenging task of financial time series forecasting. Financial markets are inherently complex and influenced by a range of factors, making accurate prediction of price movements a difficult problem. In this research, historical financial data including stock prices, volumes, and other relevant indicators are used to train CNN models aimed at capturing the underlying patterns …
Published in Current Trends in Signal Processing · Vol. 15, Issue 3, 2025 · pp. 45–55 Read article
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Encoder-Decoder Based Fine-Tuned Model for Code Doubt Solver
Abstract: As we are growing in technology, more technologically skilled persons are needed in industry. They all often rely on programming in their daily work, and when some doubts arise, they seek help from teachers to LLMs like GPT to Deepseek. However, when errors arise, then comes hectic part to troubleshoot and resolve the error. Usually, people seek help from some LLMs like GPT, or Deepseek for the solution; they give …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 35–42 Read article